Triple

T10851816
Position Surface form Disambiguated ID Type / Status
Subject Python 3.8 E256163 entity
Predicate hasPEP P13854 FINISHED
Object PEP 578
PEP 578 is a Python enhancement proposal that introduces a security audit hook framework to help monitor and control runtime events in Python applications.
 E895952 NE FINISHED

How this triple was built (4 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: PEP 578 | Statement: [Python 3.8, hasPEP, PEP 578]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: PEP 578
Context triple: [Python 3.8, hasPEP, PEP 578]
  • A. PEP 570
    PEP 570 is the Python Enhancement Proposal that introduced positional-only parameters to Python function definitions, formalizing a syntax for arguments that must be passed by position.
  • B. PEP 572
    PEP 572 is the Python proposal that introduced the “walrus operator” (:=) for assignment expressions, allowing assignment within larger expressions.
  • C. PEP 695
    PEP 695 is a Python Enhancement Proposal that introduces a new, more concise syntax for type parameter declarations to improve the language’s support for generics and static typing.
  • D. PEP 622
    PEP 622 is a Python Enhancement Proposal that introduced the design for structural pattern matching syntax later adopted in Python 3.10.
  • E. PEP 636
    PEP 636 is a Python Enhancement Proposal that serves as a tutorial-style guide to the structural pattern matching feature introduced in Python 3.10.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: PEP 578
Triple: [Python 3.8, hasPEP, PEP 578]
Generated description
PEP 578 is a Python enhancement proposal that introduces a security audit hook framework to help monitor and control runtime events in Python applications.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: PEP 578
Target entity description: PEP 578 is a Python enhancement proposal that introduces a security audit hook framework to help monitor and control runtime events in Python applications.
  • A. PEP 570
    PEP 570 is the Python Enhancement Proposal that introduced positional-only parameters to Python function definitions, formalizing a syntax for arguments that must be passed by position.
  • B. PEP 572
    PEP 572 is the Python proposal that introduced the “walrus operator” (:=) for assignment expressions, allowing assignment within larger expressions.
  • C. PEP 695
    PEP 695 is a Python Enhancement Proposal that introduces a new, more concise syntax for type parameter declarations to improve the language’s support for generics and static typing.
  • D. PEP 622
    PEP 622 is a Python Enhancement Proposal that introduced the design for structural pattern matching syntax later adopted in Python 3.10.
  • E. PEP 636
    PEP 636 is a Python Enhancement Proposal that serves as a tutorial-style guide to the structural pattern matching feature introduced in Python 3.10.
  • F. None of above. chosen

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69d6aa83d1448190a66d93c32394d21f completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d75117b76c8190b0fb216b1428c3c7 completed April 9, 2026, 7:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69e23b8b3fd48190b36e34dc19fa5193 completed April 17, 2026, 1:54 p.m.
NEDg Description generation batch_69e2453f6f008190847298f4006290f7 completed April 17, 2026, 2:35 p.m.
NED2 Entity disambiguation (via description) batch_69e288b1d64c8190b31313634b706d0a completed April 17, 2026, 7:23 p.m.
Created at: April 8, 2026, 9:20 p.m.